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          <h1 class="post-title" itemprop="name headline">A Survey of Automated Journalism</h1>
        

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        <p>A Survey of Automated Journalism<br><a id="more"></a></p>
<h2 id="文献：文本自动生成研究进展与趋势"><a href="#文献：文本自动生成研究进展与趋势" class="headerlink" title="文献：文本自动生成研究进展与趋势"></a>文献：文本自动生成研究进展与趋势</h2><p>Reference: <a href="http://www.icst.pku.edu.cn/lcwm/wanxj/files/TextGenerationSurvey.pdf" target="_blank" rel="external">http://www.icst.pku.edu.cn/lcwm/wanxj/files/TextGenerationSurvey.pdf</a></p>
<p>按照不同的输入划分，文本自动生成可包括文本到文本的生成 (text-to-text generation)、 意义到文本的生成 (meaning-to-text generation)、数据到文本的生成 (data-to-text generation) 以及图像到文本的生成 (image-to-text generation) 等。</p>
<h3 id="文本到文本的生成"><a href="#文本到文本的生成" class="headerlink" title="文本到文本的生成"></a>文本到文本的生成</h3><p>具体说来包括文本摘要 (Document Summarization)、句子压缩 (Sentence Compression)、句子融合 (Sentence Fusion)、文本复述 (Paraphrase Generation) 等。还介绍了相关期刊和机构。<br>2013 年雅虎耗资 3000 万美元收购了一项自动新闻摘要应用 Summly，标志着新闻摘要技术走向成熟。</p>
<p>2015 年 CCF 中文信息技术专委会组织了 NLPCC 评测，其中包括了面向微博的新闻摘要任务，提供了规模相对较大的样例数据和测试数据，并采用自动评价方法，吸引了多支队伍参加评测，目前这些数据可以公开获得。但上述中文摘要评测任务均针对单文档摘要任务，目前还没有业界认可的中文多文档摘要数据，这在事实上阻碍了中文自动摘要技术的发展。</p>
<p>从原文中可以看出，目前国内研究用的大规模数据集（文本到文本的生成的）还不存在。</p>
<h3 id="意义到文本的生成"><a href="#意义到文本的生成" class="headerlink" title="意义到文本的生成"></a>意义到文本的生成</h3><p>国内外相关工作都很少，难度超出我能力。</p>
<h3 id="数据到文本的生成"><a href="#数据到文本的生成" class="headerlink" title="数据到文本的生成"></a>数据到文本的生成</h3><p>应用做多且已经取得了很大的研究进展，例如基于数值数据生成天气预报文本、体育新闻、财经报道、医疗报告等。<br>很多应用通过将需要的数据填入写好的模板中来实现。</p>
<p>实现这样的系统，必须依靠科研院所和新闻出版机构的合作，新闻出版机构能够提供大量的数据和专家知识，而科研院所则擅长自然语言理解与生成的理论与方法。<br>此外，要研制一套通用的面向不同领域的数据到文本的生成系统相当复杂和困难，因此一个更好的做法是先选择一两个领域 ( 如财经、体育 ) 进行系统研制，待系统成熟后再考虑将系统迁移到其他领域。</p>
<h3 id="图像到文本的生成"><a href="#图像到文本的生成" class="headerlink" title="图像到文本的生成"></a>图像到文本的生成</h3><p>指根据给定的图像生成描述该图像内容的自然语言文本，例如新闻图像附带的标题、医学图像附属的说明、儿童教育中常见的看图说话、以及用户在微博等互联网应用中上传图片时提供的说明文字。<br>这项任务又可以分为图像标题自动生成和图像说明自动生成。</p>
<p>从图像到文本的生成技术需要集成模式识别与机器学习、计算机视觉、自然语言处理，甚至认知科学领域的研究成果，具有极高的理论研究价值和实用前景。从一定程度上讲，这一技术同图像语义标注等任务一道，已成为各大顶尖科研机构在人工智能领域综合研究实力的较量方式，必将促进其快速发展。</p>
<h2 id="网络文章：如何让人工智能学会用数据说话"><a href="#网络文章：如何让人工智能学会用数据说话" class="headerlink" title="网络文章：如何让人工智能学会用数据说话"></a>网络文章：如何让人工智能学会用数据说话</h2><p>Reference: <a href="http://www.msra.cn/zh-cn/news/features/text-generation-from-structured-data-20170314" target="_blank" rel="external">http://www.msra.cn/zh-cn/news/features/text-generation-from-structured-data-20170314</a></p>
<p>结构化数据的文本生成：用户输入结构化的数据，机器输出描述和解释结构化数据的文本。</p>
<h3 id="文本生成的技术发展的挑战："><a href="#文本生成的技术发展的挑战：" class="headerlink" title="文本生成的技术发展的挑战："></a>文本生成的技术发展的挑战：</h3><p>基于结构化数据的文本生成任务主要包括两个挑战【 4 】：<br>第一，说什么（What to say），这就意味着机器需要从输入的若干数据记录中选择要描述的记录<br>第二，怎么说（How to say），机器需对选定的数据记录，用自然语言描述出来</p>
<h3 id="评测"><a href="#评测" class="headerlink" title="评测"></a>评测</h3><p>内在（Intrinsic）评测和外在（Extrinsic）评测。</p>
<ol>
<li>内在评测关注系统生成文本的正确性、流畅性和可理解性等。内在评测方法又可分为两类：<ol>
<li>通过采用自动化的评测方法（如 BLEU, NIST 和 ROUGE 等）对比系统生成的文本和人工写作的文本之间的相似度，以此来衡量系统生成文本的质量；</li>
<li>通过调查问卷等方式，由人们从正确性、流畅性等角度出发直接对系统生成的文本进行打分，来评价系统生成文本的质量。</li>
</ol>
</li>
<li>外在评测关注于评价系统生成文本的可用性，即评价系统生成的文本对于用户完成特定任务是否有帮助。</li>
</ol>
<p>现阶段文本生成的相关工作多采用自动化的内在评测方法——即利用计算机对比系统生成文本和人工写作文本之间的相似度，原因是此类评价方法更加便捷、成本较低。而外在的评测方法成本较高，采用此类评测方法的论文较少，但是此类评测方法能更好的标示出系统的可用性。</p>
<h3 id="实现方法与技术"><a href="#实现方法与技术" class="headerlink" title="实现方法与技术"></a>实现方法与技术</h3><p>基于神经网络的方法又分为基于神经语言模型（Neural Language Model）的方法和基于神经机器翻译（Neural Machine Translation）的方法。</p>
<p>其中，Wen 等提出了 Semantic Controlled LSTM（Long Short-term Memory）模型用于对话系统中的文本生成【 8 】。该模型在标准 LSTM 的基础上引入了一个控制门读取结构化数据信息，并控制结构化数据信息在语言模型中的输出。该论文获得了 2015 年 EMNLP 会议的最佳论文。<br>Kiddon 等提出了神经清单模型（Neural Checklist Model），用于解决 RNN（Recurrent Neural Networks）模型对结构化数据中的信息重复生成的问题【 9 】。Kiddon 等将该模型应用于菜谱的生成，即输入菜名以及食材清单，机器输出相应的菜谱。基于结构化数据的文本生成存在数据稀疏的问题，即结构化数据中的许多数据值（实体名、数值等）出现次数非常少，使得模型的学习变的困难。<br>Lebret 等将拷贝动作（Copy-action）引入神经语言模型，用于解决数据稀疏的问题【 10 】。Lebret 等将该模型应用于维基百科的人物传记生成，即输入人物的信息框（Infobox），机器根据信息框中的人物信息，输出人物的文本描述。经清单模型（Neural Checklist Model），用于解决 RNN（Recurrent Neural Networks）模型对结构化数据中的信息重复生成的问题。</p>
<p>受神经机器翻译模型【 11 】的启发，Mei 等将基于结构化数据的文本生成任务视为一个翻译任务，即输入的源语言是结构化数据，输出的目标语言是文本【 3 】。很自然的，神经机器翻译模型可以解决怎么说的问题。为了进一步解决说什么的问题，Mei 等在神经机器翻译模型的基础上引入了对数据记录的重要性进行建模的机制，即越重要的数据，其先验概率越大，越有可能在文本中被表达出来。</p>
<p>基于神经语言模型的方法和基于神经机器翻译的方法在特定数据集上都取得了较大的进步，其本质仍然是 Sequence-to-sequence 方法的胜利。</p>
<h3 id="用于研究的数据"><a href="#用于研究的数据" class="headerlink" title="用于研究的数据"></a>用于研究的数据</h3><p>为了推动文本生成技术的发展，研究人员们将相关数据集共享给学术界研究使用。本文对部分数据集进行了收集和整理：</p>
<ol>
<li>斯坦福大学的 Percy<br>Liang 教授共享了一份天气预报数据集【 7 】。这份数据集包括了美国 3753 个城市（人口大于 10000）连续三天的天气预报。<br>数据集下载地址为：<a href="https://cs.stanford.edu/~pliang/data/weather-data.zip" target="_blank" rel="external">https://cs.stanford.edu/~pliang/data/weather-data.zip</a></li>
<li>德克萨斯大学奥斯汀分校的 Raymond J.<br>Mooney 教授共享了机器人足球赛的数据集【 12 】。这份数据集包括了 2036 场机器人足球赛的数据统计和评论。<br>数据集下载地址为：<a href="http://www.cs.utexas.edu/~ml/clamp/sportscasting/data.tar.gz" target="_blank" rel="external">http://www.cs.utexas.edu/~ml/clamp/sportscasting/data.tar.gz</a></li>
<li>Facebook 共享了维基百科人物传记的数据集【 10 】。这份数据集包括了 728,321 篇从维基百科获取的人物传记。<br>数据集下载地址为：<a href="https://github.com/DavidGrangier/wikipedia-biography-dataset" target="_blank" rel="external">https://github.com/DavidGrangier/wikipedia-biography-dataset</a></li>
<li>剑桥大学的 Tsung-Hsien<br>Wen 共享了基于服务的人机对话数据集【 8 】。这份数据集包括了 248 轮餐馆领域的对话和 164 轮酒店领域的对话。<br>数据集下载地址为：<a href="https://github.com/shawnwun/RNNLG/tree/master/data/original" target="_blank" rel="external">https://github.com/shawnwun/RNNLG/tree/master/data/original</a></li>
</ol>
<h3 id="总结和展望"><a href="#总结和展望" class="headerlink" title="总结和展望"></a>总结和展望</h3><p>综上，基于结构化数据的文本生成技术已经在商业领域获得了初步的成功，深度学习技术的发展和大数据的积累也推动着相关技术的进步。相信该领域会在技术、数据和商业的三重驱动下取得更大的突破。</p>
<h2 id="网页：百度-NLP-智能写作机器人"><a href="#网页：百度-NLP-智能写作机器人" class="headerlink" title="网页：百度 NLP | 智能写作机器人"></a>网页：百度 NLP | 智能写作机器人</h2><p>Reference: <a href="http://www.sohu.com/a/133249185_465975" target="_blank" rel="external">http://www.sohu.com/a/133249185_465975</a></p>
<p>据了解，目前百度智能写作文章可涵盖社会、财经、娱乐等 15 个大类，并可实现体育新闻、热点新闻等多领域全机器创作。</p>
<h3 id="实现方法与技术-1"><a href="#实现方法与技术-1" class="headerlink" title="实现方法与技术"></a>实现方法与技术</h3><p>原文中给出了宏观的计算机生成一篇文章的过程，但没有涉及深度学习技术。</p>
<p>笔者注：我在网上也没有找到相关的关于如何实现的资料。</p>
<h2 id="网络文章：会有那么一天，机器人可以写小说吗？"><a href="#网络文章：会有那么一天，机器人可以写小说吗？" class="headerlink" title="网络文章：会有那么一天，机器人可以写小说吗？"></a>网络文章：会有那么一天，机器人可以写小说吗？</h2><p>Reference: <a href="http://www.msra.cn/zh-cn/news/features/machine-writing-20170505" target="_blank" rel="external">http://www.msra.cn/zh-cn/news/features/machine-writing-20170505</a></p>
<h3 id="与写作相关的人工智能技术有哪些？"><a href="#与写作相关的人工智能技术有哪些？" class="headerlink" title="与写作相关的人工智能技术有哪些？"></a>与写作相关的人工智能技术有哪些？</h3><p>大致上又可以分成两个部分，也就是自然语言理解以及自然语言生成。</p>
<h4 id="基于规则的文本生成"><a href="#基于规则的文本生成" class="headerlink" title="基于规则的文本生成"></a>基于规则的文本生成</h4><p>这就像是我们在学习外语时，首先了解词性以及语法等规则，再依据这样的规则写出文章。</p>
<h4 id="与规则法对立的是统计法的思路"><a href="#与规则法对立的是统计法的思路" class="headerlink" title="与规则法对立的是统计法的思路"></a>与规则法对立的是统计法的思路</h4><p>就像语文老师说的，“虽然文法是对的，不过总是有例外”。规则很难囊括所有的情况。统计法根据大量的文本数据来获取统计知识，进而建立有效的语言模型。张星星（Xingxing Zhang，音译）将循环神经网络 (Recurrent Neural Network, RNN) 运用在中文古诗的生成上，但由于 RNN 在文本过长的时候容易产生梯度爆炸 (exploding gradient) 或者梯度消失 (vanishing gradient) 的问题，导致不容易学到平仄或者是押韵这样的跨句子规则，因此作者使用了卷积神经网络 (Convolution Neural Network, CNN) 将前句向量化，并且将该向量使用在句子层级的 RNN 上，藉此来达成古诗的生成。</p>
<p>2015 年，安德雷•卡帕西 (Andrej Karpathy) 在 Github 上发布了一个被称为“ char-rnn ”的简单模型，仅仅几百行代码就在文本生成上得到了令人称奇的结果。只要提供一段训练文本，这个程序就会设法从中学习字词的组合，并且有能力依葫芦画瓢地产生出与训练文本风格相符的段落。一时之间，大量的测试结果在网络上如雨后春笋般冒出，从莎士比亚到红楼梦、从金庸到汪峰，可见其简单有效。这个模型每次输入一个字 (character)，并且利用长短期记忆神经网络 (Long-Short Term Memory, LSTM) 来避开 RNN 遇到的梯度问题，可用简单的架构达到不错的效果。</p>
<p>从马尔可夫模型到神经网络，我们也把自然语言生成的层级从句子拉到了诗词（段落）。回到了本篇文章的主题，如果是写一篇文章、一篇小说，那又如何实现呢？我们前述的方法本质上都是在依据前面的字来预测下一个字，也就是说，在生成的时候其实是没有整句话的信息的。而这显然与人类在写文章的时候会先想好要讲什么是有些不同的。塞缪尔•鲍曼 (Samuel R.Bowman) 等人便提出了一个基于变分自动编码 (Variational AutoEncoder, VAE) 的模型架构，即：设法在 RNN 之内运用上全域的信息，而这个信息可以是句子的特征，也可以是句子的主题等。虽然这样的架构允许我们在生成的时候使用上整句的信息，但是在整篇文章的层级，这显然还是不够的。<strong>因此，利用现有的深度学习技术写出像《机器人写作的那一天》这样长度的小说，是不可思议的。</strong></p>
<h3 id="他们是怎么做到的？"><a href="#他们是怎么做到的？" class="headerlink" title="他们是怎么做到的？"></a>他们是怎么做到的？</h3><p>概括来说，预先设置好小说的结构，分派不同的部件完成不同结构的任务。输入参数，让这些部件根据参数自动生成句子。</p>
<h2 id="Paper-Automated-Journalism-–-AI-Applications-at-New-York-Times-Reuters-and-Other-Media-Giants"><a href="#Paper-Automated-Journalism-–-AI-Applications-at-New-York-Times-Reuters-and-Other-Media-Giants" class="headerlink" title="Paper: Automated Journalism – AI Applications at New York Times, Reuters, and Other Media Giants"></a>Paper: Automated Journalism – AI Applications at New York Times, Reuters, and Other Media Giants</h2><p><a href="https://www.techemergence.com/automated-journalism-applications/" target="_blank" rel="external">https://www.techemergence.com/automated-journalism-applications/</a><br>Question:<br>What new journalism tasks are made possible by AI?<br>Which AI applications are playing a role in augmenting the journalistic process, and which are actually replacing journalists?<br>How are newsrooms using these applications to improve the quality of news media, and how will they affect the future of journalism?</p>
<h3 id="An-Overview-of-Findings-in-Automated-Journalism"><a href="#An-Overview-of-Findings-in-Automated-Journalism" class="headerlink" title="An Overview of Findings in Automated Journalism"></a>An Overview of Findings in Automated Journalism</h3><p>AI is enhancing the newsroom in the following ways:</p>
<ol>
<li>Streamlining media workflows: AI enables journalists to focus on what they do best: reporting as illustrated by BBC ’ s Juicer.</li>
<li>Automating mundane tasks: An application such as Reuter ’ s News Tracer can track down breaking news, so that journalists are not tied down to grunt work.</li>
<li>Crunching more data: Research can be performed much faster, as shown by The New York Times Research and Development Lab ’ s Editor application.</li>
<li>Digging out media insights: Information can be correlated quickly and efficiently, such as The Washington Post ’ s Knowledge Map.</li>
<li>Eliminating fake news: Fact checking is speedy and reliable. Facebook is using AI to detect word patterns that may indicate a fake news story.</li>
<li>Generating outputs: Machines can put together reports and stories from raw data, such as Narrative Science ’ s, Quill platform, which turns data into intelligent stories.</li>
</ol>
<h3 id="The-Washington-Post-–-Automated-Journalism"><a href="#The-Washington-Post-–-Automated-Journalism" class="headerlink" title="The Washington Post – Automated Journalism"></a>The Washington Post – Automated Journalism</h3><p>Automated journalism products got their original start in more data-grounded domains like sports and finance (see the Yahoo! example below) – where raw data about news events could be transferred into coherent story, and it seems that Washington Post ’ s Heliograph is doing much of the same thing.</p>
<h2 id="Paper-Data-Driven-News-Generation-for-Automated-Journalism"><a href="#Paper-Data-Driven-News-Generation-for-Automated-Journalism" class="headerlink" title="Paper: Data-Driven News Generation for Automated Journalism"></a>Paper: Data-Driven News Generation for Automated Journalism</h2><p>Reference: <a href="https://www.cs.helsinki.fi/u/htoivone/pubs/leppanenetal_inlg_2017.pdf" target="_blank" rel="external">https://www.cs.helsinki.fi/u/htoivone/pubs/leppanenetal_inlg_2017.pdf</a></p>
<p>all facts are represented using identical data structures.</p>
<h3 id="Overview-of-the-architecture"><a href="#Overview-of-the-architecture" class="headerlink" title="Overview of the architecture"></a>Overview of the architecture</h3><img src="/2017/09/14/A-Survey-Of-Automated-Journalism/markdown-img-paste-20170914095456666.png" alt="markdown-img-paste-20170914095456666.png" title="">
<h2 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</h2><p>当前有几个大型媒体公司成功地实现了 Automated Journalism 应用，这些应用的实现都需要结构化数据、知识库或优质资源，并且依赖于预先设置好文章结构或模板。他们的这些应用我看不到深度学习的成分，其成功是复杂系统工程发挥了作用。<br>其他已成功实现的文本生成应用也都基于结构化数据。古诗的生成有个清华的团队在做，效果并不好。“ char-rnn ”模型能根据输入的文本，能依葫芦画瓢地产生出与训练文本风格相符的文本。</p>
<p>以上其中一篇论述了：</p>
<blockquote>
<p>利用现有的深度学习技术写出像《机器人写作的那一天》这样长度的小说，是不可思议的。</p>
</blockquote>
<p>基于以上，我认为不适合开展此任务。同时，作为初学者，我还是先复现别人的研究成功，这样才能取得最快的进步。</p>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#文献：文本自动生成研究进展与趋势"><span class="nav-number">1.</span> <span class="nav-text">文献：文本自动生成研究进展与趋势</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#文本到文本的生成"><span class="nav-number">1.1.</span> <span class="nav-text">文本到文本的生成</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#意义到文本的生成"><span class="nav-number">1.2.</span> <span class="nav-text">意义到文本的生成</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#数据到文本的生成"><span class="nav-number">1.3.</span> <span class="nav-text">数据到文本的生成</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#图像到文本的生成"><span class="nav-number">1.4.</span> <span class="nav-text">图像到文本的生成</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#网络文章：如何让人工智能学会用数据说话"><span class="nav-number">2.</span> <span class="nav-text">网络文章：如何让人工智能学会用数据说话</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#文本生成的技术发展的挑战："><span class="nav-number">2.1.</span> <span class="nav-text">文本生成的技术发展的挑战：</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#评测"><span class="nav-number">2.2.</span> <span class="nav-text">评测</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#实现方法与技术"><span class="nav-number">2.3.</span> <span class="nav-text">实现方法与技术</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#用于研究的数据"><span class="nav-number">2.4.</span> <span class="nav-text">用于研究的数据</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#总结和展望"><span class="nav-number">2.5.</span> <span class="nav-text">总结和展望</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#网页：百度-NLP-智能写作机器人"><span class="nav-number">3.</span> <span class="nav-text">网页：百度 NLP | 智能写作机器人</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#实现方法与技术-1"><span class="nav-number">3.1.</span> <span class="nav-text">实现方法与技术</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#网络文章：会有那么一天，机器人可以写小说吗？"><span class="nav-number">4.</span> <span class="nav-text">网络文章：会有那么一天，机器人可以写小说吗？</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#与写作相关的人工智能技术有哪些？"><span class="nav-number">4.1.</span> <span class="nav-text">与写作相关的人工智能技术有哪些？</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#基于规则的文本生成"><span class="nav-number">4.1.1.</span> <span class="nav-text">基于规则的文本生成</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#与规则法对立的是统计法的思路"><span class="nav-number">4.1.2.</span> <span class="nav-text">与规则法对立的是统计法的思路</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#他们是怎么做到的？"><span class="nav-number">4.2.</span> <span class="nav-text">他们是怎么做到的？</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#Paper-Automated-Journalism-–-AI-Applications-at-New-York-Times-Reuters-and-Other-Media-Giants"><span class="nav-number">5.</span> <span class="nav-text">Paper: Automated Journalism – AI Applications at New York Times, Reuters, and Other Media Giants</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#An-Overview-of-Findings-in-Automated-Journalism"><span class="nav-number">5.1.</span> <span class="nav-text">An Overview of Findings in Automated Journalism</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#The-Washington-Post-–-Automated-Journalism"><span class="nav-number">5.2.</span> <span class="nav-text">The Washington Post – Automated Journalism</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#Paper-Data-Driven-News-Generation-for-Automated-Journalism"><span class="nav-number">6.</span> <span class="nav-text">Paper: Data-Driven News Generation for Automated Journalism</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#Overview-of-the-architecture"><span class="nav-number">6.1.</span> <span class="nav-text">Overview of the architecture</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#总结"><span class="nav-number">7.</span> <span class="nav-text">总结</span></a></li></ol></div>
            

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          isfetched = true;
          $('.popup').detach().appendTo('.header-inner');
          var datas = isXml ? $("entry", res).map(function() {
            return {
              title: $("title", this).text(),
              content: $("content",this).text(),
              url: $("url" , this).text()
            };
          }).get() : res;
          var input = document.getElementById(search_id);
          var resultContent = document.getElementById(content_id);
          var inputEventFunction = function() {
            var searchText = input.value.trim().toLowerCase();
            var keywords = searchText.split(/[\s\-]+/);
            if (keywords.length > 1) {
              keywords.push(searchText);
            }
            var resultItems = [];
            if (searchText.length > 0) {
              // perform local searching
              datas.forEach(function(data) {
                var isMatch = false;
                var hitCount = 0;
                var searchTextCount = 0;
                var title = data.title.trim();
                var titleInLowerCase = title.toLowerCase();
                var content = data.content.trim().replace(/<[^>]+>/g,"");
                var contentInLowerCase = content.toLowerCase();
                var articleUrl = decodeURIComponent(data.url);
                var indexOfTitle = [];
                var indexOfContent = [];
                // only match articles with not empty titles
                if(title != '') {
                  keywords.forEach(function(keyword) {
                    function getIndexByWord(word, text, caseSensitive) {
                      var wordLen = word.length;
                      if (wordLen === 0) {
                        return [];
                      }
                      var startPosition = 0, position = [], index = [];
                      if (!caseSensitive) {
                        text = text.toLowerCase();
                        word = word.toLowerCase();
                      }
                      while ((position = text.indexOf(word, startPosition)) > -1) {
                        index.push({position: position, word: word});
                        startPosition = position + wordLen;
                      }
                      return index;
                    }

                    indexOfTitle = indexOfTitle.concat(getIndexByWord(keyword, titleInLowerCase, false));
                    indexOfContent = indexOfContent.concat(getIndexByWord(keyword, contentInLowerCase, false));
                  });
                  if (indexOfTitle.length > 0 || indexOfContent.length > 0) {
                    isMatch = true;
                    hitCount = indexOfTitle.length + indexOfContent.length;
                  }
                }

                // show search results

                if (isMatch) {
                  // sort index by position of keyword

                  [indexOfTitle, indexOfContent].forEach(function (index) {
                    index.sort(function (itemLeft, itemRight) {
                      if (itemRight.position !== itemLeft.position) {
                        return itemRight.position - itemLeft.position;
                      } else {
                        return itemLeft.word.length - itemRight.word.length;
                      }
                    });
                  });

                  // merge hits into slices

                  function mergeIntoSlice(text, start, end, index) {
                    var item = index[index.length - 1];
                    var position = item.position;
                    var word = item.word;
                    var hits = [];
                    var searchTextCountInSlice = 0;
                    while (position + word.length <= end && index.length != 0) {
                      if (word === searchText) {
                        searchTextCountInSlice++;
                      }
                      hits.push({position: position, length: word.length});
                      var wordEnd = position + word.length;

                      // move to next position of hit

                      index.pop();
                      while (index.length != 0) {
                        item = index[index.length - 1];
                        position = item.position;
                        word = item.word;
                        if (wordEnd > position) {
                          index.pop();
                        } else {
                          break;
                        }
                      }
                    }
                    searchTextCount += searchTextCountInSlice;
                    return {
                      hits: hits,
                      start: start,
                      end: end,
                      searchTextCount: searchTextCountInSlice
                    };
                  }

                  var slicesOfTitle = [];
                  if (indexOfTitle.length != 0) {
                    slicesOfTitle.push(mergeIntoSlice(title, 0, title.length, indexOfTitle));
                  }

                  var slicesOfContent = [];
                  while (indexOfContent.length != 0) {
                    var item = indexOfContent[indexOfContent.length - 1];
                    var position = item.position;
                    var word = item.word;
                    // cut out 100 characters
                    var start = position - 20;
                    var end = position + 80;
                    if(start < 0){
                      start = 0;
                    }
                    if (end < position + word.length) {
                      end = position + word.length;
                    }
                    if(end > content.length){
                      end = content.length;
                    }
                    slicesOfContent.push(mergeIntoSlice(content, start, end, indexOfContent));
                  }

                  // sort slices in content by search text's count and hits' count

                  slicesOfContent.sort(function (sliceLeft, sliceRight) {
                    if (sliceLeft.searchTextCount !== sliceRight.searchTextCount) {
                      return sliceRight.searchTextCount - sliceLeft.searchTextCount;
                    } else if (sliceLeft.hits.length !== sliceRight.hits.length) {
                      return sliceRight.hits.length - sliceLeft.hits.length;
                    } else {
                      return sliceLeft.start - sliceRight.start;
                    }
                  });

                  // select top N slices in content

                  var upperBound = parseInt('1');
                  if (upperBound >= 0) {
                    slicesOfContent = slicesOfContent.slice(0, upperBound);
                  }

                  // highlight title and content

                  function highlightKeyword(text, slice) {
                    var result = '';
                    var prevEnd = slice.start;
                    slice.hits.forEach(function (hit) {
                      result += text.substring(prevEnd, hit.position);
                      var end = hit.position + hit.length;
                      result += '<b class="search-keyword">' + text.substring(hit.position, end) + '</b>';
                      prevEnd = end;
                    });
                    result += text.substring(prevEnd, slice.end);
                    return result;
                  }

                  var resultItem = '';

                  if (slicesOfTitle.length != 0) {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + highlightKeyword(title, slicesOfTitle[0]) + "</a>";
                  } else {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + title + "</a>";
                  }

                  slicesOfContent.forEach(function (slice) {
                    resultItem += "<a href='" + articleUrl + "'>" +
                      "<p class=\"search-result\">" + highlightKeyword(content, slice) +
                      "...</p>" + "</a>";
                  });

                  resultItem += "</li>";
                  resultItems.push({
                    item: resultItem,
                    searchTextCount: searchTextCount,
                    hitCount: hitCount,
                    id: resultItems.length
                  });
                }
              })
            };
            if (keywords.length === 1 && keywords[0] === "") {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-search fa-5x" /></div>'
            } else if (resultItems.length === 0) {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-frown-o fa-5x" /></div>'
            } else {
              resultItems.sort(function (resultLeft, resultRight) {
                if (resultLeft.searchTextCount !== resultRight.searchTextCount) {
                  return resultRight.searchTextCount - resultLeft.searchTextCount;
                } else if (resultLeft.hitCount !== resultRight.hitCount) {
                  return resultRight.hitCount - resultLeft.hitCount;
                } else {
                  return resultRight.id - resultLeft.id;
                }
              });
              var searchResultList = '<ul class=\"search-result-list\">';
              resultItems.forEach(function (result) {
                searchResultList += result.item;
              })
              searchResultList += "</ul>";
              resultContent.innerHTML = searchResultList;
            }
          }

          if ('auto' === 'manual') {
            input.addEventListener('input', inputEventFunction);
          } else {
            $('.search-icon').click(inputEventFunction);
            input.addEventListener('keypress', function (event) {
              if (event.keyCode === 13) {
                inputEventFunction();
              }
            });
          }

          // remove loading animation
          $(".local-search-pop-overlay").remove();
          $('body').css('overflow', '');

          proceedsearch();
        }
      });
    }

    // handle and trigger popup window;
    $('.popup-trigger').click(function(e) {
      e.stopPropagation();
      if (isfetched === false) {
        searchFunc(path, 'local-search-input', 'local-search-result');
      } else {
        proceedsearch();
      };
    });

    $('.popup-btn-close').click(onPopupClose);
    $('.popup').click(function(e){
      e.stopPropagation();
    });
    $(document).on('keyup', function (event) {
      var shouldDismissSearchPopup = event.which === 27 &&
        $('.search-popup').is(':visible');
      if (shouldDismissSearchPopup) {
        onPopupClose();
      }
    });
  </script>





  

  

  

  
  
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